Professors Cook and Weisberg will continue to investigate the role that individual observations or cases play in controlling data analyses based on full-rank linear models. Primary emphasis will be placed on (1) developing and comparing measures of influence for groups of cases when the least-squares estimate of the parameter vector is of principal importance, (2) developing measures of influence for secondary phases of an analysis (e.g., model selection, transformations and prediction), (3) extending the tools developed to non least-squares situations, principally ridge regression and (4) formulating possible corrective steps that can appropriately reduce the influence of cases. These problems can be important in the analysis of data obtained from a wide variety of bio-medical applications, such as clinical trials.